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Imaging the Volve ocean-bottom field data with the Upside-down Rayleigh-Marchenko method
  • Ning Wang,
  • Matteo Ravasi
Ning Wang
King Abdullah University of Science and Technology

Corresponding Author:[email protected]

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Matteo Ravasi
King Abdullah University of Science and Technology
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Abstract

Ocean-bottom seismic acquisitions are gaining widespread popularity for various subsurface applications. However, the high cost of these systems often necessitates receiver geometries with large intervals between ocean-bottom cables or nodes. The upside-down Rayleigh-Marchenko (UD-RM) method has been recently proposed as an effective solution for accurate redatuming and imaging of sparse seabed data. In this paper, we present the first successful application of the UD-RM method to field data. We demonstrate that in the presence of a shallow seabed, an improved data pre-processing workflow is necessary to generate more accurate input wavefields than those produced by the workflow in the original paper. To validate the proposed processing workflow, the UD-RM method is initially tested on a synthetic dataset mimicking the Volve field data, followed by its application to a 2D line of the Volve ocean-bottom cable dataset. Subsequently, the field dataset is subsampled by retaining only 25\% of the total receivers to numerically validate the UD-RM method’s capability to handle sparse receiver arrays. The resulting images reveal that the UD-RM method, when paired with our enhanced data processing workflow, can effectively handle surface-related multiples, internal multiples, and sparse receiver arrays, producing accurate imaging results without the need for costly and labor-intensive multiple removal processes. Finally, we provide theoretical insights and numerical evidence supporting the necessity of source-side deghosting prior to redatuming. While a pre-processing workflow that omits source-side deghosting can offers some practical advantages, we show that this ultimately produces blurrier images compared to those obtained using source-side deghosted input data.
29 Oct 2024Submitted to ESS Open Archive
30 Oct 2024Published in ESS Open Archive